Buckets:

hf-doc-build
/

doc

Files
xet
hf-doc-build/doc / diffusers /v0.43.0 /zh /optimization /attention_backends.html
mishig
2 days ago
download
raw
36.8 kB
<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;注意力后端&quot;,&quot;local&quot;:&quot;attention-backends&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;set_attention_backend&quot;,&quot;local&quot;:&quot;setattentionbackend&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;attention_backend 上下文管理器&quot;,&quot;local&quot;:&quot;attentionbackend-context-manager&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;信任远程内核&quot;,&quot;local&quot;:&quot;trusting-remote-kernels&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;检查&quot;,&quot;local&quot;:&quot;checks&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;可用的后端&quot;,&quot;local&quot;:&quot;available-backends&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"/>
		<link href="/docs/diffusers/main/en/_app/immutable/entry/start.0S0_iEoG.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/chunks/I9eooN77.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/chunks/Bupkjr9f.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/entry/app.CPdOxCbw.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/chunks/CyvZ3FS5.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/chunks/l0QnnOED.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/chunks/DnVIJfC5.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/chunks/DsnmJJEf.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/nodes/0.DteTvmQZ.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/chunks/CoSrdQax.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/nodes/330.CbiFN3pb.js" rel="modulepreload">
		<link href="/docs/diffusers/main/en/_app/immutable/chunks/DSPDl1gz.js" rel="modulepreload">
		<!--gxcprv--><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Attention backends&quot;,&quot;local&quot;:&quot;attention-backends&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;set_attention_backend&quot;,&quot;local&quot;:&quot;setattentionbackend&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;attention_backend context manager&quot;,&quot;local&quot;:&quot;attentionbackend-context-manager&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Trusting remote kernels&quot;,&quot;local&quot;:&quot;trusting-remote-kernels&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Checks&quot;,&quot;local&quot;:&quot;checks&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Available backends&quot;,&quot;local&quot;:&quot;available-backends&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"/><!---->
		<link href="/docs/diffusers/main/en/_app/immutable/assets/0.tn0RQdqM.css" rel="modulepreload"> <!--[--><!--[0--><!--[--><!--[0--><!--[--><p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: inline-end; margin-inline-start: 10px; display: inline-flex; position: relative; z-index: 10;"><div dir="ltr" class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----></span> <span dir="auto">Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg><!----></button></div> <!--[-1--><!--]--></div><!----> <!--[0--><h1 class="relative group"><a id="attention-backends" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#attention-backends"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->注意力后端<!----></span></h1><!--]--><!----> <blockquote class="note"><p>注意力分发器（attention dispatcher）是一项实验性功能。如果您有任何反馈或遇到问题，请提交一个 issue。</p></blockquote> <p>Diffusers 通过其 <em>attention dispatcher</em> 提供了多种经过优化的注意力算法，这些算法在内存和计算效率方面表现更佳。该调度器充当路由器，用于管理和切换不同的注意力实现，并为与这些实现进行交互提供统一的接口。</p> <p>请参阅下表，了解可用的注意力机制家族，并参阅 <a href="#available-backends">可用后端</a> 部分以获取更完整的列表。</p> <table><thead><tr><th>注意力机制</th><th>主要功能</th></tr></thead><tbody><tr><td>FlashAttention</td><td>通过分块和重计算来最小化内存读写</td></tr><tr><td>ROCm 的 AI Tensor Engine</td><td>针对 AMD ROCm 加速器优化的 FlashAttention 实现</td></tr><tr><td>SageAttention</td><td>将注意力量化为 int8</td></tr><tr><td>PyTorch 原生</td><td>使用 <a href="./fp16#scaled-dot-product-attention">scaled_dot_product_attention</a> 的内置 PyTorch 实现</td></tr><tr><td>xFormers</td><td>支持多种注意力内核的内存高效注意力</td></tr></tbody></table> <p>本指南将向您展示如何设置和使用不同的注意力后端。</p> <!--[1--><h2 class="relative group"><a id="setattentionbackend" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#setattentionbackend"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->set_attention_backend<!----></span></h2><!--]--><!----> <p><a href="/docs/diffusers/v0.43.0/zh/api/models/overview#diffusers.ModelMixin.set_attention_backend">set_attention_backend()</a> 方法会遍历模型中的所有模块，并设置要使用的相应注意力后端。注意力后端的设置会一直生效，直到调用 <a href="/docs/diffusers/v0.43.0/zh/api/models/overview#diffusers.ModelMixin.reset_attention_backend">reset_attention_backend()</a> 为止。</p> <p>以下示例演示了如何启用来自 <a href="https://github.com/huggingface/kernels" rel="nofollow"><code>kernels</code></a> 库的 FlashAttention-3 的 <code>_flash_3_hub</code> 实现，该实现允许您即时使用 Hub 上优化的计算内核，而无需任何配置。</p> <blockquote class="note"><p>FlashAttention-3 至少需要 Ampere 架构的 GPU。</p></blockquote> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600  " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> 已复制</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> QwenImagePipeline

pipeline = QwenImagePipeline.from_pretrained(
    <span class="hljs-string">&quot;Qwen/Qwen-Image&quot;</span>, dtype=torch.bfloat16, device_map=<span class="hljs-string">&quot;cuda&quot;</span>  <span class="hljs-comment"># or &quot;mps&quot;, &quot;xpu&quot;, &quot;cpu&quot;</span>
)
pipeline.transformer.set_attention_backend(<span class="hljs-string">&quot;_flash_3_hub&quot;</span>)

prompt = <span class="hljs-string">&quot;&quot;&quot;
cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
&quot;&quot;&quot;</span>
pipeline(prompt).images[<span class="hljs-number">0</span>]<!----></pre></div><!----> <p>要恢复默认的注意力后端，请调用 <a href="/docs/diffusers/v0.43.0/zh/api/models/overview#diffusers.ModelMixin.reset_attention_backend">reset_attention_backend()</a>。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600  " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> 已复制</div><!----></button><!----></div> <pre class="language-py "><!---->pipeline.transformer.reset_attention_backend()<!----></pre></div><!----> <!--[1--><h2 class="relative group"><a id="attentionbackend-context-manager" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#attentionbackend-context-manager"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->attention_backend 上下文管理器<!----></span></h2><!--]--><!----> <p><a href="https://github.com/huggingface/diffusers/blob/5e181eddfe7e44c1444a2511b0d8e21d177850a0/src/diffusers/models/attention_dispatch.py#L225" rel="nofollow">attention_backend</a> 上下文管理器会在上下文内为模型临时设置一个注意力后端。在上下文之外，将使用默认的注意力机制（即 PyTorch 原生的缩放点积注意力）。如果您希望为流水线中的不同部分使用不同的后端，或者想要测试不同的后端，这一功能会非常有用。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600  " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> 已复制</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> QwenImagePipeline

pipeline = QwenImagePipeline.from_pretrained(
    <span class="hljs-string">&quot;Qwen/Qwen-Image&quot;</span>, dtype=torch.bfloat16, device_map=<span class="hljs-string">&quot;cuda&quot;</span>  <span class="hljs-comment"># or &quot;mps&quot;, &quot;xpu&quot;, &quot;cpu&quot;</span>
)
prompt = <span class="hljs-string">&quot;&quot;&quot;
cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
&quot;&quot;&quot;</span>

<span class="hljs-keyword">with</span> attention_backend(<span class="hljs-string">&quot;_flash_3_hub&quot;</span>):
    image = pipeline(prompt).images[<span class="hljs-number">0</span>]<!----></pre></div><!----> <blockquote class="tip"><p>大多数注意力后端支持 <code>torch.compile</code>，且不会导致图中断，可用于进一步加速推理。</p></blockquote> <!--[1--><h2 class="relative group"><a id="trusting-remote-kernels" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trusting-remote-kernels"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->信任远程内核<!----></span></h2><!--]--><!----> <p>Hub 后端以及其他基于内核的功能（例如 <a href="../quantization/gguf">GGUF</a> 和 <a href="../quantization/nunchaku">Nunchaku Lite</a>）会通过 <a href="https://github.com/huggingface/kernels" rel="nofollow"><code>kernels</code></a> 从 Hub 下载计算内核，并在本地执行其代码。</p> <p>默认情况下，<code>kernels</code> 仅在其发布者属于 Hub 上的受信任内核发布者时才会加载内核。在 <a href="https://huggingface.co/kernels-community" rel="nofollow"><code>kernels-community</code></a> 组织下发布的所有内核均受信任，因此 Diffusers 无需任何额外配置即可加载它们。<code>_flash_3_hub</code>、<code>flash_hub</code>、<code>sage_hub</code> 以及其他 Hub 注意力后端均解析到 <code>kernels-community</code> 仓库。</p> <p>来自其他发布者的内核未经审核。加载此类内核会下载并运行 Diffusers 无法保证其安全性的代码，因此 Diffusers 默认将其禁用，除非您通过 <code>DIFFUSERS_TRUST_REMOTE_KERNELS</code> 环境变量明确选择启用。设置该变量后，Diffusers 会将 <code>trust_remote_code=True</code> 传递给 <code>kernels</code>，使其也能加载来自不受信任发布者的内核。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600  " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> 已复制</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">export</span> DIFFUSERS_TRUST_REMOTE_KERNELS=<span class="hljs-literal">true</span><!----></pre></div><!----> <p>仅在检查内核仓库后再启用此选项，因为它会授予已下载代码在您的机器上运行的权限。若未启用，从不受信任的发布者加载内核将引发错误。Diffusers 会自行执行此检查，因此该检查同样适用于早于 <code>trust_remote_code</code> 参数引入的 <code>kernels&lt;0.14.0</code>。设置 <code>DIFFUSERS_DISABLE_REMOTE_CODE=true</code> 可全局禁用远程代码，并且其优先级高于 <code>DIFFUSERS_TRUST_REMOTE_KERNELS</code>。</p> <!--[1--><h2 class="relative group"><a id="checks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#checks"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->检查<!----></span></h2><!--]--><!----> <p>注意力分发器包含调试检查，可在问题引发故障之前捕获常见错误。</p> <ol><li>设备检查会验证查询、键和值张量是否位于同一设备上。</li> <li>数据类型检查会确认张量具有匹配的数据类型，并且使用 bfloat16 或 float16。</li> <li>形状检查用于验证张量维度，并防止将注意力掩码与因果标志混用。</li></ol> <p>通过设置 <code>DIFFUSERS_ATTN_CHECKS</code> 环境变量来启用这些检查。由于检查会为每个注意力操作增加额外开销，因此默认情况下是禁用的。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600  " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> 已复制</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">export</span> DIFFUSERS_ATTN_CHECKS=<span class="hljs-built_in">yes</span><!----></pre></div><!----> <p>这些检查现在会在每次注意力操作之前运行。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600  " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> 已复制</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">import</span> torch

query = torch.randn(<span class="hljs-number">1</span>, <span class="hljs-number">10</span>, <span class="hljs-number">8</span>, <span class="hljs-number">64</span>, dtype=torch.bfloat16, device=<span class="hljs-string">&quot;cuda&quot;</span>)  <span class="hljs-comment"># or &quot;mps&quot;, &quot;xpu&quot;, &quot;cpu&quot;</span>
key = torch.randn(<span class="hljs-number">1</span>, <span class="hljs-number">10</span>, <span class="hljs-number">8</span>, <span class="hljs-number">64</span>, dtype=torch.bfloat16, device=<span class="hljs-string">&quot;cuda&quot;</span>)
value = torch.randn(<span class="hljs-number">1</span>, <span class="hljs-number">10</span>, <span class="hljs-number">8</span>, <span class="hljs-number">64</span>, dtype=torch.bfloat16, device=<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-keyword">try</span>:
    <span class="hljs-keyword">with</span> attention_backend(<span class="hljs-string">&quot;flash&quot;</span>):
        output = dispatch_attention_fn(query, key, value)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;✓ Flash Attention works with checks enabled&quot;</span>)
<span class="hljs-keyword">except</span> Exception <span class="hljs-keyword">as</span> e:
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;✗ Flash Attention failed: <span class="hljs-subst">{e}</span>&quot;</span>)<!----></pre></div><!----> <p>您也可以直接配置注册表。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600  " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> 已复制</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">from</span> diffusers.models.attention_dispatch <span class="hljs-keyword">import</span> _AttentionBackendRegistry

_AttentionBackendRegistry._checks_enabled = <span class="hljs-literal">True</span><!----></pre></div><!----> <!--[1--><h2 class="relative group"><a id="available-backends" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#available-backends"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->可用的后端<!----></span></h2><!--]--><!----> <p>请参阅下表，了解可用的注意力后端及其变体的完整列表。</p> <details><summary>Expand</summary> <table><thead><tr><th>后端名称</th><th>家族</th><th>描述</th></tr></thead><tbody><tr><td><code>native</code></td><td><a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend" rel="nofollow">PyTorch 原生</a></td><td>使用 PyTorch 的 scaled_dot_product_attention 的默认后端</td></tr><tr><td><code>flex</code></td><td><a href="https://docs.pytorch.org/docs/stable/nn.attention.flex_attention.html#module-torch.nn.attention.flex_attention" rel="nofollow">FlexAttention</a></td><td>PyTorch FlexAttention 实现</td></tr><tr><td><code>_native_cudnn</code></td><td><a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend" rel="nofollow">PyTorch 原生</a></td><td>CuDNN 优化的注意力</td></tr><tr><td><code>_native_efficient</code></td><td><a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend" rel="nofollow">PyTorch 原生</a></td><td>内存高效注意力</td></tr><tr><td><code>_native_flash</code></td><td><a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend" rel="nofollow">PyTorch 原生</a></td><td>PyTorch 的 FlashAttention</td></tr><tr><td><code>_native_math</code></td><td><a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend" rel="nofollow">PyTorch 原生</a></td><td>基于数学的注意力（回退方案）</td></tr><tr><td><code>_native_npu</code></td><td><a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend" rel="nofollow">PyTorch 原生</a></td><td>NPU 优化的注意力</td></tr><tr><td><code>_native_xla</code></td><td><a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend" rel="nofollow">PyTorch 原生</a></td><td>XLA 优化的注意力</td></tr><tr><td><code>flash</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>FlashAttention-2</td></tr><tr><td><code>flash_hub</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>来自内核的 FlashAttention-2</td></tr><tr><td><code>flash_varlen</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>可变长度 FlashAttention</td></tr><tr><td><code>flash_varlen_hub</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>来自内核的变长 FlashAttention</td></tr><tr><td><code>aiter_fa2_hub</code></td><td><a href="https://github.com/ROCm/aiter" rel="nofollow">面向 ROCm 的 AI Tensor Engine</a></td><td>来自 kernels 的 AMD ROCm 版 FlashAttention-2</td></tr><tr><td><code>aiter_sage_hub</code></td><td><a href="https://github.com/ROCm/aiter" rel="nofollow">面向 ROCm 的 AI Tensor Engine</a></td><td>来自 kernels 的 AMD RDNA4 (gfx1201) 量化注意力 (INT8 QK, FP8 PV)</td></tr><tr><td><code>flash_4_hub</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>FlashAttention-4</td></tr><tr><td><code>_flash_3</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>FlashAttention-3</td></tr><tr><td><code>_flash_varlen_3</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>可变长度 FlashAttention-3</td></tr><tr><td><code>_flash_3_hub</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>来自 kernels 的 FlashAttention-3</td></tr><tr><td><code>_flash_3_varlen_hub</code></td><td><a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">FlashAttention</a></td><td>来自内核的变长 FlashAttention-3</td></tr><tr><td><code>sage</code></td><td><a href="https://github.com/thu-ml/SageAttention" rel="nofollow">SageAttention</a></td><td>量化注意力（INT8 QK）</td></tr><tr><td><code>sage_hub</code></td><td><a href="https://github.com/thu-ml/SageAttention" rel="nofollow">SageAttention</a></td><td>来自内核的量化注意力（INT8 QK）</td></tr><tr><td><code>sage_blackwell_hub</code></td><td><a href="https://github.com/thu-ml/SageAttention" rel="nofollow">SageAttention</a></td><td>来自 kernels 的 SageAttention3 FP4 注意力，适用于 SM120 Blackwell GPU</td></tr><tr><td><code>sage_varlen</code></td><td><a href="https://github.com/thu-ml/SageAttention" rel="nofollow">SageAttention</a></td><td>可变长度 SageAttention</td></tr><tr><td><code>_sage_qk_int8_pv_fp8_cuda</code></td><td><a href="https://github.com/thu-ml/SageAttention" rel="nofollow">SageAttention</a></td><td>INT8 QK + FP8 PV (CUDA)</td></tr><tr><td><code>_sage_qk_int8_pv_fp8_cuda_sm90</code></td><td><a href="https://github.com/thu-ml/SageAttention" rel="nofollow">SageAttention</a></td><td>INT8 QK + FP8 PV (SM90)</td></tr><tr><td><code>_sage_qk_int8_pv_fp16_cuda</code></td><td><a href="https://github.com/thu-ml/SageAttention" rel="nofollow">SageAttention</a></td><td>INT8 QK + FP16 PV (CUDA)</td></tr><tr><td><code>_sage_qk_int8_pv_fp16_triton</code></td><td><a href="https://github.com/thu-ml/SageAttention" rel="nofollow">SageAttention</a></td><td>INT8 QK + FP16 PV (Triton)</td></tr><tr><td><code>xformers</code></td><td><a href="https://github.com/facebookresearch/xformers" rel="nofollow">xFormers</a></td><td>内存高效注意力</td></tr></tbody></table></details> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/diffusers/blob/main/docs/source/en/optimization/attention_backends.md" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg><!----> <span><span class="underline">Update</span> on GitHub</span></a><!----> <p></p><!--]--><!----><!--]--><!--]--><!--]--> <!--[-1--><!--]--><!--]-->
			
			<script>
				{
					__sveltekit_1qsdbwf = {
						base: "/docs/diffusers/main/en",
						assets: "/docs/diffusers/main/en"
					};
					const element = document.currentScript.parentElement;
					Promise.all([
						import("/docs/diffusers/main/en/_app/immutable/entry/start.0S0_iEoG.js"),
						import("/docs/diffusers/main/en/_app/immutable/entry/app.CPdOxCbw.js")
					]).then(([kit, app]) => {
						kit.start(app, element, {
							node_ids: [0, 330],
							data: [null,null],
							form: null,
							error: null
						});
					});
				}
			</script>
		
Xet Storage Details

Size:: 36.8 kB
Xet hash:: e20ec8c143899e11f037aab13addb081b8c3629a324b5b63e23f7fd7d4d43571
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.